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引用次数: 0

摘要

云连续体是传统云向多个实体(如物联网设备、边缘或雾节点)的扩展,这些实体提供分析、处理、存储和数据生成能力[1]。认知云连续体,即人工智能支持的云连续体,旨在通过从已部署服务的监控和管理中学习,或应用人工智能技术进行动态负载平衡,以优化能耗、资源使用或网络流量,通过无缝集成不同的计算和数据环境,自动适应日益增长的复杂性和数据洪流。为实现这一目标,正在进行若干努力。我们将重点关注与耦合联邦学习机制和智能资源发现相关的最新结果,以实现能够在云和边缘上运行的自适应托管环境,异常检测中的机器学习或用于分布式流处理的透明计算[2],[3],[4]。
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Cognitive Cloud Continuum : Plenary Talk
Cloud Continuum is the extension of the traditional Cloud towards multiple entities like Internet of Things devices, Edge or Fog nodes that provide analysis, processing, storage, and data generation capabilities [1]. Cognitive Cloud Continuum, i.e. AI-enabled Cloud continuum, aims to automatically adapt to the growing complexity and data deluge by integrating seamlessly diverse computing and data environments by learning from monitoring and management of deployed services or applying AI techniques for dynamic load balancing to optimize energy consumption, resource usage or network traffic. To achieve this aim several efforts are underway. We will focus on the recent results related to coupling federated learning mechanisms and intelligent resource discovery to achieve an adaptive hosting environment capable of running both on Cloud and close to the Edge, machine learning in anomaly detection, or transprecision computing for distributed stream processing [2], [3], [4].
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